From Raw Data to Actionable Insights, Instantly
In machine learning and analytics, up to 60% of project time is spent cleaning, preparing, and structuring data. AutoData removes this bottleneck. It automates data preparation and synthetic data generation, turning raw datasets into structured, feature-rich, and analysis-ready assets.
Every job runs in a dedicated high-CPU environment for maximum speed.
Your data is always encrypted and protected throughout every pipeline stage.
Output files are ready to train machine learning and deep learning models.
Powering AutoData with industry-leading cloud and data infrastructure
Azure Cloud Partner
GCP Partner
Data & AI Partner
Seven automated steps, each designed to refine and enhance your data.
Executes web searches, API requests, and LLM queries to contextually fill missing values, validates existing content against independently sourced answers, and overwrites errors or appends new columns based on preference.
Standardizes currencies to USD, unifies dates to MM/DD/YYYY, converts percentages to decimals, clears cells with NaN values, and offers 10+ other fixations for format-consistent data.
Scans all columns; tokenizes texts, audios, and images; encodes categories; and converts dates into meaningful time-based values ensuring the entire dataset is consistent and ready for machine learning.
Tests multiple imputation and removal strategies for handling gaps, runs quick model checks, and automatically selects the method with the lowest prediction error.
Automates scaling by applying several industry-standard techniques, evaluating performance, and selecting the most effective approach for balanced feature representation.
Identifies overlaps, removes repetitive columns, and detects underlying similarity patterns. The result is a leaner and more powerful dataset.
Learns the "digital DNA" of your optimized dataset and generates new synthetic points that are statistically indistinguishable from the original.
Each job delivers a dedicated results folder containing everything you need.
A clean, numeric, scaled, and sufficiently sampled dataset ready for ML-model training.
CSV files from each stage, providing a full and auditable lineage of transformations.
A concise report that documents pipeline steps, key transformations, runtime, and shows how raw data was converted into AI-ready outputs.
With AutoData, your data team focuses on insights, not repetitive work.